MSc thesis topics Derek Karssenberg Version May 2020 Earth Surface and Water Hydrology Earth Surface and Water Geohazards and Earth observation Environmental Justice Supervision: Prof Dr Derek Karssenberg (Utrecht University) In cooperation with: Julius Centre, UMCU and with team members of the Global Geo Health Data Center (http://www.gghdc.nl). Description: Human exposure to our environment has considerable effects on human health. The spatial distribution of human exposures to environmental variables such as air pollution, urban green, noise and fast-food restaurants, is believed to reflect differences in income, ethnicity, unemployment and education-levels. This study aims to explore the patterns of the environmental exposures across the Netherlands and identify relations with the neighbourhood deprivation (income, ethnicity, unemployment and education- levels). The results of this study would have implications in restoring environmental justice and consequently reduce health inequalities amongst different socio-economic groups. The thesis will include implementation of human exposure assessment techniques to be applied across the Dutch population and methods to compare spatial patterns in human exposure and neighbourhood deprivation. Location: Utrecht University Period: To be determined Number of students: 1-2 Programme / track: Preferably Natural Hazards and Earth Observation but other tracks may fit as well. Prerequisites: Courses in geographical information science and statistics is a requirement. Preferably basic knowledge in scripting (programming), remote sensing and/or simulation modelling (content of project can be adjusted to your background). Contact / info: Derek Karssenberg ([email protected]), Anna-Maria Ntarladima ([email protected])
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MSc thesis topics Derek Karssenberg
Version May 2020
Earth Surface and Water Hydrology
Earth Surface and Water Geohazards and Earth observation
Environmental Justice
Supervision: Prof Dr Derek Karssenberg (Utrecht University)
In cooperation with: Julius Centre, UMCU and with team members of the Global Geo Health Data Center (http://www.gghdc.nl).
Description:
Human exposure to our environment has considerable effects on human health. The spatial distribution of human exposures to environmental variables such as air pollution,
urban green, noise and fast-food restaurants, is believed to reflect differences in
income, ethnicity, unemployment and education-levels. This study aims to explore the
patterns of the environmental exposures across the Netherlands and identify relations with the neighbourhood deprivation (income, ethnicity, unemployment and education-
levels). The results of this study would have implications in restoring environmental
justice and consequently reduce health inequalities amongst different socio-economic
groups. The thesis will include implementation of human exposure assessment techniques to be applied across the Dutch population and methods to compare spatial
patterns in human exposure and neighbourhood deprivation.
Location: Utrecht University
Period: To be determined
Number of students: 1-2
Programme / track: Preferably Natural Hazards and Earth Observation but other tracks may fit as well.
Prerequisites: Courses in geographical information science and statistics
is a requirement. Preferably basic knowledge in scripting
(programming), remote sensing and/or simulation modelling (content of project can be adjusted to your
Algorithms for environmental modeling are at the heart of any raster-based environment model. The environmental modeller combines these core model building
blocks to build a unique model. There are many different environmental modelling
algorithms, some of which are also found in geographic information systems (GIS).
Until around 2005, CPU cores found in computers doubled in clock speed about every two years. Environmental modellers who wanted to use more complex modelling rules
and/or larger data sets, just had to buy a new computer to decrease the increased
model run times. That is not the case anymore and so model run times keep increasing
with added model complexity and data. Because CPU cores are not getting much faster anymore, hardware vendors have been
adding additional CPU cores to their CPU’s. One obvious way to solve the issue of
increasing model run times is to make models use the multiple CPU cores. This requires
a reimplementation of the above-mentioned environmental modelling algorithms. This project is about parallizing one or more environmental modelling algorithms. Some
of these algorithms are very easy to parallize, and some are not. In this project you will
look into parallizing one or more algorithms from the latter category. You will design
one or more approaches to parallize the algorithm and, depending on your interest and background, test these approaches by implementing them.
This work is highly relevant, because the results may be used in a new implementation
of our own library of modelling algorithms. Faster algorithms will have obvious benefits
for the modellers and you can make a very concrete contribution to this. Supervision: You will be supervised by a team of experienced modellers and software
engineers. They will provide you with a description of the sequential version of each
algorithm and help you getting up to speed quickly. This team will at least consist of Dr.
Derek Karssenberg and Drs. Kor de Jong.
Location: Utrecht University
Period: To be determined
Number of students: 1-3
Programme / track: Earth Surface and Water: Hydrology or Coastal dynamics
and fluvial systems, or Geohazards and Earth observation
Prerequisites: preferably courses in spatio-temporal modelling, geoinformatics, computer science
Environmental modelling tools are today an important tool to construct spatio-temporal
models. They outperform system programming languages as a model development
environment regarding programming errors and implementation time and are therefore
suitable for domain specialists like hydrologists, climatologists or ecologists. Nevertheless, these tools have mostly focused on the syntax of modelling languages
ignoring the semantic aspect of models, i.e. the meaning of the inputs, functions and
outputs of a component model. Without a formal definition of the semantics of model
components it is almost impossible to provide generic principles for coupling component models.
The coupling of specialized component models is certainly a requirement for the
construction of integrated models, as these represent a more holistic view on
environmental processes. Therefore, the development of a formal definition of model components is required. A formal approach is provided by ontologies, which describe a
conceptual domain, usually consisting of a set of statements that define concepts and
relationships between concepts. While first steps towards ontologies for environmental
models exist (e.g. Williams, M et al 2009, Lake, R.et al 2004) most of these approaches do not provide a complete description of all aspects of inputs and outputs. Research on
the development of an ontology describing the whole domain of spatio-temporal models,
including various modelling paradigms, spatial domains, and application domains is still
required. The research will be incorporated in an ongoing research project of integrated model
development. Several research questions that are appropriate for a MSc thesis are
available:
• What is the current state of formal descriptions of model inputs and outputs, what are limitations and potentials of those approaches
• Development (design and implementation) of or extension of an exisiting
ontology suitable for coupling spatio-temporal model components
• Development (design and implementation) of a user interface for creating and modifying ontology descriptions by model developer
• Evaluating the possibilities of auto-generating ontology descriptions for existing
models by software applications
• Assessing conversion problems of environmental variables occurring in the coupling of model components including different temporal and spatial
resolutions, units, coverage, ...
Own proposals are welcome.
Location: Utrecht University
Period: To be determined
Number of students: 1-2
Programme / track: Earth Surface and Water: Hydrology or Coastal dynamics
and fluvial systems, or Geohazards and Earth observation
Prerequisites: courses in spatio-temporal modelling, geostatistics,
remote sensing, hydrology, geomorphology, and/or natural hazards (content of project can be adjusted to
In our view, a modelling language is a language for expressing environmental models, by modellers. Modellers are domain experts who are not necessarily knowledgeable or
interested in software development. They need an environment with a high level of
abstraction. A modelling language, like a script language or a graphical language for
example, provides the means for the domain expert to express his ideas about the phenomena being modelled. Most domain experts are not able to express such ideas in
lower level languages like C++, C#, Java or even Python. The use of these languages
require the domain expert to know things that are not directly related to expressing a
model, like managing computer memory, managing files, handling errors. Another reason to provide a modelling environment directly to the domain expert, instead of
asking a software developer to develop models for the domain expert, is that important
decisions that have to be made during the development of the model get taken by the
domain expert, instead of the developer. Like software development, model development is a highly iterative process, and decisions about the implementation need
to be made continuously during the development of a model. Only for the most trivial
models can the domain expert provide the software developer with the full specification
of the model beforehand. In most cases the requirements of the model get adjusted continuously, based on the model’s performance.
Modellers mostly construct models along one of two modelling paradigms: field based or
agent based. In the field based approach, phenomena are considered as spatially
continuous, and spatial variation is represented by changes in the attribute value. Examples of fields are air temperature or elevation. In the agent based approach (also
individual based, feature based, or object based approach), phenomena are represented
as bounded objects that can be mobile. Spatial variation is represented by the
distribution of objects in space. Although many landscape systems require to combine the field and agent based approaches, it is notably hard to do so in a model. This is
mainly due to modelling languages being monolithic: they are either build around the
field based or agent based paradigm. Integrating the two approaches requires coupling
different modelling frameworks, which can be error prone, difficult, and time consuming. To overcome this problem, this study aims at developing a modelling language that
integrates the two approaches. The envisioned language should provide functions that
operate on fields and/or agents, in a similar fashion. This will allow modellers to
construct heterogeneous models consisting of agents and fields, in one single modelling language. Depending on your background, you can focus on designing concepts of such
a language (e.g. the syntax), implementing a prototype (in your preferred programming
language), or implementing a case study model that can be used to benchmark such a
language. This is an interesting study if you like to combine your knowledge in spatio-temporal
modelling and computer science or GIS. It gives you the opportunity to work in a multi
disciplinary team consisting of environmental scientists and (PCRaster) software
Earth Surface and Water Geohazards and Earth observation
Early-warning signals of desertification (or recovery)
Supervision: Prof. Dr. Derek Karssenberg (Utrecht University)
In cooperation with: Researchers in Spain
Description:
Landscape systems may undergo abrupt transitions as a result of a gradual change in
system drivers. Such regime shifts, or critical transitions, are often considered
undesirable because they cause large changes in the landscape that are often irreversible. A well-known regime shift in land surface systems is desertification, i.e. the
shift from a vegetated landscape to a largely unvegetated landscape, often with
degraded soils and increased erosion. The process of desertification is often abrupt,
while it may be driven by a rather gradual increase in grazing intensity. At a certain threshold grazing intensity, biomass starts to decrease, which results in increased
throughfall and runoff, causing increased runoff erosion, reducing soil thickness, which
again has a negative effect on biomass growth. This positive feedback loop results in a
relatively abrupt degradation at the grazing intensity threshold. It is notably hard to detect this upcoming regime shift, because mean values of the
system state variables (e.g. soil thickness, discharge, vegetation biomass) show little
change before a transition occurs. This problem has sparked research focused on finding
alternative properties of the system that show a more marked change before a transition is coming. It has been shown that such so-called early-warning signals exist,
more specifically higher-order statistics of state variables (e.g. instead of the mean
value of discharge, the variance; instead of the mean vegetation biomass the spatial
variation in biomass). Thus far, however, the existence of such early warning signals is mainly shown for virtual realities, i.e. modelled hillslopes. The aim of this study is to
investigate the existence of early-warning signals in the real-world. You will address the
questions of 1) What are the statistical properties of vegetation cover, soil moisture
and/or discharge of various catchments at different stages of soil degradation or soil recovery? 2) Can differences in statistical properties be explained by the occurrence of
(or upcoming) system shifts?
You will answer these questions by a statistical analysis of time series of high-resolution
remote sensing data, including soil moisture, leaf area index and vegetation cover and possibly hydrographs for the same area. We have access to a data set in an area close
to Zaragoza, Spain, and cooperation with the research group in Zaragoza is an option if
you choose for this topic. Results of this analysis will be combined with information on
the occurence of soil degradation or recovery in the same area, possibly by making a field visit. If time allows (or if the study is done by two students), the study can be
extended by a modelling study investigating the occurence of early-warning signals in
similar, modelled, systems. Most of the data is already available.
Location: Utrecht University, possibility to visit Spain to collect
additional data
Period: To be determined
Number of students: 1-2
Programme / track: Earth Surface Hydrology or Natural Hazards and Earth
Earth Surface and Water Coastal dynamics and fluvial systems
Earth Surface and Water Geohazards and Earth observation
Evaluating hydrograph and sedigraph characteristics as early-warning signals of soil degradation
Supervision: Prof Dr Derek Karssenberg (Utrecht University)
In cooperation with: -
Description:
Landscape systems my undergo abrupt transitions as a result of a gradual change in
system drivers. Such regime shifts, or critical transitions, are often considered
undesirable because they cause large changes in the landscape that are often
irreversible. A well known regime shift in land surface systems is the shift from thick hillslope soils with high biomass to soils with almost no soil cover and low biomass. This
process of land degradation is often abrupt, while it may be driven by a rather gradual
increase in grazing intensity. At a certain threshold grazing intensity, biomass starts to
decrease, which results in increased throughfall and runoff, causing increased runoff erosion, reducing soil thickness, which again has a negative effect on biomass growth.
This positive feedback loop results in a relatively abrupt degradation at the grazing
intensity threshold.
It is notably hard to detect this upcoming regime shift, because mean values of the system state variables (e.g. soil thickness, discharge, vegetation biomass) show little
change before a transition occurs. This problem has sparked research focused on finding
alternative properties of the system that show a more marked change before a
transition is coming. It has been shown that such so-called early-warning signals exist, more specifically higher-order statistics of state variables (e.g. instead of the mean
value of discharge, the variance; instead of the mean vegetation biomass the spatial
variation in biomass).
In this study you will evaluate whether statistical properties of hydrographs and/or sedigraphs can be used as early-warning signals for soil degradation. This is done in a
modelling study. An existing hillslope evolution model that runs over time periods of
hundreds to thousands of years is used to simulate the shift from a vegetated hillslope
with thick soils to a degraded hillslope. Its main output is a timeseries of hillslope geometries (topographical surface, development of gullies, regolith thickness) and
vegetation coverage. This output is used as input to an event-based hydrological model
that is capable of modelling the complete hydrograph for individual events. It is
expected that the hydrograph properties will change in advance of the upcoming shift towards a degraded system. To analyse, this, statistical properties (e.g. peakflow, time
to peak, total discharge) will be calculated of hydrographs.
This is an interesting topic if you like a fundamental approach to hydrology, with a focus
on modelling. The study is quite innovative, and you will be able to position your work in a (recently emerged) large body of literature on critical shifts and early-warning signals.
Location: Utrecht University
Period: To be determined
Number of students: 1
Programme / track: Earth Surface Hydrology or Natural Hazards and Earth Observation
Prerequisites: courses in spatio-temporal modelling, hydrology,
Earth Surface and Water Coastal dynamics and fluvial systems
Earth Surface and Water Geohazards and Earth observation
Identifying systemic change in catchment hydrology
Supervision: Prof Dr Derek Karssenberg (Utrecht University)
In cooperation with: -
Description:
Temporal change in landscape systems is mostly studied with a focus on temporal variation in system states (e.g. groundwater level, discharge, denudation rate). These
changes are driven by the landscape system, which includes all driving forces active in
the landscape. In most cases, this system is considered constant, which implies that it is
assumed that the processes and their interconnections remain the same. For instance, model calibration against observational data (aiming at parameter identification) mostly
assumes that the set of modelled processes and their associated parameters remain the
same: a single set of equations and parameters is assumed to represent the past and
future behavior of the system. In many cases, however, the system itself may change over time, due to external forces or due to internal mechanisms in the landscape that
completely alter the system processes and system behavior. An example is the land use
system. Land use change is driven by many factors, including land prices, transport
costs, housing costs, environmental properties. In many cases, these factors are considered constant and land use change is modelled with the same set of rules for all
time steps. In reality however, many of these factors may change due to
implementation of new technology or environmental laws, which implies systemic
change of the land use system. In this study you will address systemic change in the hydrological system. Hydrological
models are nowadays-important tools for forecasting drought and flooding. To reduce
uncertainty in forecasts, these models are calibrated against observational data, in most
cases river discharge time series. As noted above, it is mostly assumed that one unique set of model parameters can be used to represent hydrologic behavior for all time
periods (both past and future simulations, for all years). In reality, however, systemic
change will occur, which will be associated with changes in parameter values. Systemic
change in catchment hydrology may be due to changes in land use (causing changes in interception, infiltration), changes in geomorphology (causing changes in soil depth and
subsurface hydrology), or other changes such as implementation of new reservoirs. In
this study you will identify these changes by an inverse method, by calibrating a
catchment model separately for each time period (typically one year) in a series of time periods. This will result in a time series of parameter values (i.e., a value for each year),
that represent the temporal change in the hydrologic system. Following this approach
you can address the questions of 1) What is the temporal change in parameter values of
a catchment model? 2) Is it possible to relate these temporal changes to changes in the modelled catchment (e.g. landuse, geomorphology, reservoirs) that caused this
systemic change in the hydrology? 3) What are the possible implications of this systemic
change for forecasts of catchment discharge?
For this study you will use existing calibration techniques on an existing data set (large time series data are available for multiple decennia) and model (one of the data sets
available, most likely the Danube catchment). This is an interesting topic if you would
like to apply your knowledge in hydrology in a challenging rather innovative study. You
will get technical support from staff and PhD students at our institute to get the calibrations running. The content of the study can be adjusted to your interests (e.g.
you could also study systemic change in other landscape systems).
Location: Utrecht University, possibility to cooperate with Münster
University
Period: To be determined
Number of students: 1-2
Programme / track: Earth Surface Hydrology or Natural Hazards and Earth
Observation
Prerequisites: courses in (stochastic) hydrology, spatio-temporal modelling (content of project can be adjusted to your
In Mediterranean regions, water availability is low and depends on runoff generated in
mountain areas. However, a marked decline in river discharges has been observed in
the last century, related to i) decreasing precipitation and increasing temperature and ii)
increasing expansion of vegetation in the headwaters due to land abandonment. On the other hand, increasing water consumption for domestic, industrial and agricultural uses
is occurring in the lowlands. Future water management will need to cope with these
changing scenarios in order to ensure water supply.
In this study we will focus on the impact of re-vegetation in the headwaters on future trends of water availability. Questions addressed include: how will re-vegetation affect
water availability? What is the seasonality of river flows? What is the water demand in
the lowlands? What is the spatio-temporal pattern of the resulting water stress?
The research will be carried out in the Ebro basin, an example representative for large Mediterranean rivers. An existing process-based distributed hydrological model
developed within the PCRaster Python framework and calibrated in a small catchment in
the Pyrenees will be used. The model will be run under future land cover and climate
change scenarios for a larger area in the Pyrenees. The discharge simulated in the upstream area will be compared to future downstream demand to calculate water
stress.
Location: Utrecht University, possibility to visit the research area
(Ebro basin) to collect additional data
Period: To be determined
Number of students: 1-3
Programme / track: Earth Surface Hydrology or Natural Hazards and Earth
Observation
Prerequisites: courses in spatio-temporal modelling, hydrology, geomorphology, and/or natural hazards
Mediterranean mountains have been largely affected by agricultural abandonment and
subsequent vegetation recovery. The resulting expansion of forest and shrubs has
modified the hydrological behavior of these areas, with significant impact on runoff
production. Forecasting the effect on stream flow response of such vegetation recovery is particularly relevant in the Mediterranean region, where water resources are scarce
and uneven, and they rely on runoff generated in mountain areas.
With this purpose, a process-based distributed hydrological model was developed within
the PCRaster Python framework. The model has been calibrated in a past agricultural catchment (2.8 km2) in the Spanish Pyrenees, monitored by the Instituto Pirenacio de
Ecologia (CSIC). In order to reproduce realistic vegetation recovery scenarios, we need
to determine the soil and vegetation parameters for several stages of land
abandonment. The aim of this research is to characterize several stages of land abandonment in terms of vegetation and soil properties, and to identify their effect on
the stream flow response. The research will include:
• Fieldwork in the study area (Spanish Pyrenees): at each site (representing a
stage of land abandonment) we will collect data related to vegetation and soil characteristics
• Statistical analysis of the field data
• Simulation of the hydrological response of the catchment under different re-
vegetation scenarios, based on the data collected in the field Detailed content of the research can be discussed and tailored to your background. The
fieldwork will take place preferably in September.
Location: Utrecht University, possibility to visit the research area
(Ebro basin) to collect additional data
Period: To be determined
Number of students: 1-3
Programme / track: Earth Surface Hydrology or Natural Hazards and Earth
Observation
Prerequisites: courses in spatio-temporal modelling, hydrology,
Earth Surface and Water Coastal dynamics and fluvial systems
Earth Surface and Water Geohazards and Earth observation
Earth, Life and Climate Integrated stratigraphy and sedimentary systems
Earth Surface and Water Hydrology
Earth Surface and Water Coastal dynamics and fluvial systems
Earth Surface and Water Geohazards and Earth observation
Earth, Life and Climate Integrated stratigraphy and sedimentary systems
Earth, Life and Climate Climate reconstruction
Title: Personal exposure to air pollution in megacities of the world
Supervision: Prof Dr Derek Karssenberg, Dr Oliver Schmitz
In cooperation with: Institute for Risk Assessment Sciences (Utrecht University)
Description:
Air pollution is one of the major concerns for human health. The effect of air pollution on
health is often estimated using personal exposure to air pollution. This is the exposure to air pollution aggregated along the space-time path visited by an individual. An
important question is how megacities in the world differ regarding personal exposure of
their population. In this topic you will try to answer this question by calculating personal
exposure of the entire population of a number of megacities in the world, using publicly available information. Air pollution will be mapped by downscaling (increasing the level
of detail) remotely sensed air pollution products to a spatial resolution of approximately
10 m using existing land use regression models, using open streetmap data as input.
Space-time paths visited by individuals are estimated using location of houses, possibly enriched with census data or other high-resolution information on location of dwellings.
Then, air pollution is aggregated for these locations, for each individual in the
population. This results in distributions of personal exposure for the population of the
city. The objective is to do this for a number of major cities in the world. This requires good skills in programming GIS operations, e.g. using Python and/or PCRaster, ArcGIS.
Location: Utrecht University, cooperation possible with Instititute for Risk Assessment Sciences, University Medical Centre
Utrecht
Period: Any period is possible.
Number of students: 1-2 students
Programme / track: Earth Surface Hydrology or Natural Hazards and Earth
Observation
Prerequisites: Experience with programming (scripting, e.g. Python),
background in GIS, spatio-temporal modelling, (geo-statistics).
In cooperation with: Partners in https://globalgeohealthdatacenter.com
Description:
Malaria poses serious social and health burdens in many tropical and subtropical
countries. It is widely acknowledged that the malaria transmission dynamics are closely related to climatic and environmental factors. With the burgeoning availability of global
Earth observation data, the opportunity arises to examine how a data-driven approach
could contribute to global malaria epidemic risk assessing and warning. The objective of
this study is to examine whether Earth observations and a data driven approach can improve understanding of the linkage between weather, surface water, vegetation, and
Malaria occurrence.
Potentially, we will use open surface water products derived from high resolution (30m) satellite imagery [1] with long time series. The corresponding paper has been published
in Nature in 2016 [2]. This dataset will provide us information about spatiotemporal
dynamics of global surface water to link it with Malaria incidence. The time series of
precipitation volumes may be from TRMM (Tropical Rainfall Measuring Missing, flew from 1997-2015) products and GPMM (Global precipitation measurement Mission, launched
on Feb.27, 2014)[3]. The Land surface temperature will be derived from the Landsat
satellite product or using existing products. Besides, the project will also include local
scale identification of malaria-weather relationships using precipitation and temperature data from local rainfall gauge network and meteorological stations.
The research questions are, but not confined to: what are the independent and joint
effects of the above-mentioned variables on Malaria incidence? Can the Malaria epidemics be predicted? What is the potential of Earth observations in global Malaria
mapping and warning? Can the locally identified relationship be extended to global
mapping? How does climate change (e.g. in extreme weather events, temperature,
precipitation) affect Malaria epidemics?
This interdisciplinary study will expose you to a wide range of global Earth observation
products and novel data analytics methods. You will learn and develop novel
spatiotemporal statistical algorithms to analyse dynamics of global climatic and environmental factors such as precipitation, land surface temperature, water surface,
and identify their joined effects and spatiotemporal predicting power on Malaria
epidemics.
[1] Global surface water. https://global-surface-water.appspot.com/
[2] Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution
mapping of global surface water and its long-term changes. Nature, 540(7633), 418-